Overview

Dataset statistics

Number of variables16
Number of observations7241
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory905.2 KiB
Average record size in memory128.0 B

Variable types

NUM16

Warnings

Lunation Number is highly correlated with Year and 1 other fieldsHigh correlation
Year is highly correlated with Lunation Number and 1 other fieldsHigh correlation
Saros Number is highly correlated with Year and 1 other fieldsHigh correlation
Penumbral Magnitude is highly correlated with Eclipse Type and 2 other fieldsHigh correlation
Eclipse Type is highly correlated with Penumbral Magnitude and 1 other fieldsHigh correlation
Umbral Magnitude is highly correlated with Eclipse Type and 1 other fieldsHigh correlation
Penumbral Eclipse Duration (m) is highly correlated with Penumbral MagnitudeHigh correlation
Lunation Number has unique values Unique
Hours has 309 (4.3%) zeros Zeros
Minutes has 119 (1.6%) zeros Zeros
Seconds has 114 (1.6%) zeros Zeros
Latitude has 95 (1.3%) zeros Zeros

Reproduction

Analysis started2020-12-18 20:55:47.359014
Analysis finished2020-12-18 20:56:36.149104
Duration48.79 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3000
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.650463
Minimum1
Maximum3000
Zeros0
Zeros (%)0.0%
Memory size56.6 KiB
2020-12-19T02:26:36.310362image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile147
Q1747
median1505
Q32256
95-th percentile2856
Maximum3000
Range2999
Interquartile range (IQR)1509

Descriptive statistics

Standard deviation867.2689429
Coefficient of variation (CV)0.5771594689
Kurtosis-1.197002641
Mean1502.650463
Median Absolute Deviation (MAD)755
Skewness0.001698597337
Sum10880692
Variance752155.4194
MonotocityIncreasing
2020-12-19T02:26:36.528354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
72550.1%
 
66050.1%
 
7450.1%
 
131150.1%
 
167650.1%
 
278350.1%
 
47550.1%
 
187950.1%
 
296850.1%
 
124650.1%
 
Other values (2990)719199.3%
 
ValueCountFrequency (%) 
13< 0.1%
 
22< 0.1%
 
32< 0.1%
 
42< 0.1%
 
540.1%
 
ValueCountFrequency (%) 
30003< 0.1%
 
29992< 0.1%
 
29982< 0.1%
 
29973< 0.1%
 
29962< 0.1%
 

Month
Real number (ℝ≥0)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.510841044
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size56.6 KiB
2020-12-19T02:26:36.738241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.452547282
Coefficient of variation (CV)0.5302766967
Kurtosis-1.208270433
Mean6.510841044
Median Absolute Deviation (MAD)3
Skewness-0.004744153832
Sum47145
Variance11.92008273
MonotocityNot monotonic
2020-12-19T02:26:36.928270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
16238.6%
 
76168.5%
 
36168.5%
 
56168.5%
 
126168.5%
 
106128.5%
 
86058.4%
 
66018.3%
 
95938.2%
 
115918.2%
 
Other values (2)115215.9%
 
ValueCountFrequency (%) 
16238.6%
 
25617.7%
 
36168.5%
 
45918.2%
 
56168.5%
 
ValueCountFrequency (%) 
126168.5%
 
115918.2%
 
106128.5%
 
95938.2%
 
86058.4%
 

Day
Real number (ℝ≥0)

Distinct31
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.82088109
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size56.6 KiB
2020-12-19T02:26:37.111360image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.801844693
Coefficient of variation (CV)0.556343521
Kurtosis-1.192283945
Mean15.82088109
Median Absolute Deviation (MAD)8
Skewness0.008227244265
Sum114559
Variance77.47247
MonotocityNot monotonic
2020-12-19T02:26:37.286127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
42693.7%
 
172633.6%
 
82613.6%
 
112603.6%
 
202583.6%
 
72573.5%
 
132493.4%
 
182463.4%
 
212433.4%
 
262423.3%
 
Other values (21)469364.8%
 
ValueCountFrequency (%) 
12253.1%
 
22163.0%
 
32383.3%
 
42693.7%
 
52092.9%
 
ValueCountFrequency (%) 
311532.1%
 
302273.1%
 
292353.2%
 
282323.2%
 
272363.3%
 

Hours
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.53473277
Minimum0
Maximum23
Zeros309
Zeros (%)4.3%
Memory size56.6 KiB
2020-12-19T02:26:37.602613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median11
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.923910796
Coefficient of variation (CV)0.6002662509
Kurtosis-1.203761336
Mean11.53473277
Median Absolute Deviation (MAD)6
Skewness-0.001274299034
Sum83523
Variance47.94054071
MonotocityNot monotonic
2020-12-19T02:26:37.851590image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
193514.8%
 
33384.7%
 
153374.7%
 
113314.6%
 
233284.5%
 
63264.5%
 
23134.3%
 
103124.3%
 
123094.3%
 
03094.3%
 
Other values (14)398755.1%
 
ValueCountFrequency (%) 
03094.3%
 
12583.6%
 
23134.3%
 
33384.7%
 
42994.1%
 
ValueCountFrequency (%) 
233284.5%
 
222884.0%
 
212793.9%
 
202974.1%
 
193514.8%
 

Minutes
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.30023477
Minimum0
Maximum59
Zeros119
Zeros (%)1.6%
Memory size56.6 KiB
2020-12-19T02:26:38.046461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q114
median29
Q344
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.23293972
Coefficient of variation (CV)0.5881502264
Kurtosis-1.196868385
Mean29.30023477
Median Absolute Deviation (MAD)15
Skewness0.01329207751
Sum212163
Variance296.9742113
MonotocityNot monotonic
2020-12-19T02:26:38.255983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
451512.1%
 
261442.0%
 
381411.9%
 
141351.9%
 
61351.9%
 
281341.9%
 
551331.8%
 
81331.8%
 
101321.8%
 
131301.8%
 
Other values (50)587381.1%
 
ValueCountFrequency (%) 
01191.6%
 
11281.8%
 
21101.5%
 
31201.7%
 
41211.7%
 
ValueCountFrequency (%) 
591161.6%
 
581221.7%
 
57961.3%
 
561211.7%
 
551331.8%
 

Seconds
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.21060627
Minimum0
Maximum59
Zeros114
Zeros (%)1.6%
Memory size56.6 KiB
2020-12-19T02:26:38.459848image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median29
Q344
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.30017654
Coefficient of variation (CV)0.592256675
Kurtosis-1.201505896
Mean29.21060627
Median Absolute Deviation (MAD)15
Skewness0.02379319885
Sum211514
Variance299.2961085
MonotocityNot monotonic
2020-12-19T02:26:38.659681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
421492.1%
 
111442.0%
 
171432.0%
 
131422.0%
 
321401.9%
 
271381.9%
 
391381.9%
 
251371.9%
 
101351.9%
 
201341.9%
 
Other values (50)584180.7%
 
ValueCountFrequency (%) 
01141.6%
 
11211.7%
 
21331.8%
 
31191.6%
 
41161.6%
 
ValueCountFrequency (%) 
591301.8%
 
581081.5%
 
571191.6%
 
561221.7%
 
551111.5%
 

Delta T (s)
Real number (ℝ)

Distinct4461
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2621.691893
Minimum-6
Maximum10519
Zeros4
Zeros (%)0.1%
Memory size56.6 KiB
2020-12-19T02:26:38.865118image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile11
Q1330
median1484
Q33955
95-th percentile9103
Maximum10519
Range10525
Interquartile range (IQR)3625

Descriptive statistics

Standard deviation2871.212348
Coefficient of variation (CV)1.095175354
Kurtosis0.3378049279
Mean2621.691893
Median Absolute Deviation (MAD)1379
Skewness1.183161381
Sum18983671
Variance8243860.348
MonotocityNot monotonic
2020-12-19T02:26:39.056225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12680.9%
 
10550.8%
 
8470.6%
 
16430.6%
 
9410.6%
 
24410.6%
 
11400.6%
 
7390.5%
 
-6360.5%
 
17350.5%
 
Other values (4451)679693.9%
 
ValueCountFrequency (%) 
-6360.5%
 
-5160.2%
 
-470.1%
 
-350.1%
 
-240.1%
 
ValueCountFrequency (%) 
105191< 0.1%
 
105151< 0.1%
 
105141< 0.1%
 
105101< 0.1%
 
105051< 0.1%
 

Lunation Number
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct7241
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6145.789118
Minimum-24719
Maximum12378
Zeros1
Zeros (%)< 0.1%
Memory size56.6 KiB
2020-12-19T02:26:39.235503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-24719
5-th percentile-22918
Q1-15494
median-6115
Q33169
95-th percentile10593
Maximum12378
Range37097
Interquartile range (IQR)18663

Descriptive statistics

Standard deviation10726.59529
Coefficient of variation (CV)-1.745356875
Kurtosis-1.196987467
Mean-6145.789118
Median Absolute Deviation (MAD)9332
Skewness0.001680212221
Sum-44501659
Variance115059846.5
MonotocityStrictly increasing
2020-12-19T02:26:39.409436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
85281< 0.1%
 
-96421< 0.1%
 
-34451< 0.1%
 
-177821< 0.1%
 
-239271< 0.1%
 
117491< 0.1%
 
-136921< 0.1%
 
-198371< 0.1%
 
83051< 0.1%
 
75531< 0.1%
 
Other values (7231)723199.9%
 
ValueCountFrequency (%) 
-247191< 0.1%
 
-247141< 0.1%
 
-247131< 0.1%
 
-247081< 0.1%
 
-247021< 0.1%
 
ValueCountFrequency (%) 
123781< 0.1%
 
123771< 0.1%
 
123721< 0.1%
 
123661< 0.1%
 
123601< 0.1%
 

Saros Number
Real number (ℝ≥0)

HIGH CORRELATION

Distinct142
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.7794504
Minimum41
Maximum183
Zeros0
Zeros (%)0.0%
Memory size56.6 KiB
2020-12-19T02:26:39.598135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile63
Q188
median113
Q3137
95-th percentile163
Maximum183
Range142
Interquartile range (IQR)49

Descriptive statistics

Standard deviation31.06466874
Coefficient of variation (CV)0.2754461796
Kurtosis-0.8550127725
Mean112.7794504
Median Absolute Deviation (MAD)25
Skewness0.009616162837
Sum816636
Variance965.013644
MonotocityNot monotonic
2020-12-19T02:26:39.774028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
102841.2%
 
101831.1%
 
120831.1%
 
121821.1%
 
103821.1%
 
138821.1%
 
119821.1%
 
100791.1%
 
84781.1%
 
137781.1%
 
Other values (132)642888.8%
 
ValueCountFrequency (%) 
412< 0.1%
 
423< 0.1%
 
433< 0.1%
 
4490.1%
 
45100.1%
 
ValueCountFrequency (%) 
1832< 0.1%
 
1823< 0.1%
 
18170.1%
 
180100.1%
 
179110.2%
 

Eclipse Type
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.161303687
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size56.6 KiB
2020-12-19T02:26:39.935948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4405528
Coefficient of variation (CV)0.5864875489
Kurtosis-1.33843189
Mean4.161303687
Median Absolute Deviation (MAD)2
Skewness-0.1956195633
Sum30132
Variance5.956297969
MonotocityNot monotonic
2020-12-19T02:26:40.088961image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
5253635.0%
 
1239833.1%
 
685011.7%
 
86218.6%
 
76148.5%
 
4881.2%
 
3680.9%
 
2660.9%
 
ValueCountFrequency (%) 
1239833.1%
 
2660.9%
 
3680.9%
 
4881.2%
 
5253635.0%
 
ValueCountFrequency (%) 
86218.6%
 
76148.5%
 
685011.7%
 
5253635.0%
 
4881.2%
 

Gamma
Real number (ℝ)

Distinct6458
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005489186576
Minimum-1.5827
Maximum1.572
Zeros0
Zeros (%)0.0%
Memory size56.6 KiB
2020-12-19T02:26:40.380792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1.5827
5-th percentile-1.409
Q1-0.7862
median0.0074
Q30.7929
95-th percentile1.4262
Maximum1.572
Range3.1547
Interquartile range (IQR)1.5791

Descriptive statistics

Standard deviation0.9108999188
Coefficient of variation (CV)165.9444266
Kurtosis-1.213690675
Mean0.005489186576
Median Absolute Deviation (MAD)0.7892
Skewness0.0002465601251
Sum39.7472
Variance0.8297386621
MonotocityNot monotonic
2020-12-19T02:26:40.559134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.777640.1%
 
0.882240.1%
 
1.196440.1%
 
-0.414840.1%
 
-0.52773< 0.1%
 
0.69563< 0.1%
 
0.14633< 0.1%
 
-1.31793< 0.1%
 
-0.68873< 0.1%
 
-1.15593< 0.1%
 
Other values (6448)720799.5%
 
ValueCountFrequency (%) 
-1.58271< 0.1%
 
-1.57841< 0.1%
 
-1.57581< 0.1%
 
-1.57311< 0.1%
 
-1.57232< 0.1%
 
ValueCountFrequency (%) 
1.5721< 0.1%
 
1.57181< 0.1%
 
1.57061< 0.1%
 
1.571< 0.1%
 
1.56711< 0.1%
 

Penumbral Magnitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6409
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.417879616
Minimum0.0006
Maximum2.9089
Zeros0
Zeros (%)0.0%
Memory size56.6 KiB
2020-12-19T02:26:40.744691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.0006
5-th percentile0.1312
Q10.6783
median1.4157
Q32.1386
95-th percentile2.7211
Maximum2.9089
Range2.9083
Interquartile range (IQR)1.4603

Descriptive statistics

Standard deviation0.8323359487
Coefficient of variation (CV)0.5870286443
Kurtosis-1.203334837
Mean1.417879616
Median Absolute Deviation (MAD)0.7295
Skewness0.02152918818
Sum10266.8663
Variance0.6927831316
MonotocityNot monotonic
2020-12-19T02:26:40.932586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.714440.1%
 
2.820340.1%
 
2.14523< 0.1%
 
1.59743< 0.1%
 
0.94023< 0.1%
 
0.12413< 0.1%
 
1.13293< 0.1%
 
1.56063< 0.1%
 
0.87173< 0.1%
 
2.13693< 0.1%
 
Other values (6399)720999.6%
 
ValueCountFrequency (%) 
0.00061< 0.1%
 
0.00071< 0.1%
 
0.00141< 0.1%
 
0.00221< 0.1%
 
0.00271< 0.1%
 
ValueCountFrequency (%) 
2.90891< 0.1%
 
2.9041< 0.1%
 
2.89921< 0.1%
 
2.89281< 0.1%
 
2.89251< 0.1%
 

Umbral Magnitude
Real number (ℝ)

HIGH CORRELATION

Distinct6408
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.399387916
Minimum-1.0947
Maximum1.8821
Zeros0
Zeros (%)0.0%
Memory size56.6 KiB
2020-12-19T02:26:41.108879image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1.0947
5-th percentile-0.8842
Q1-0.334
median0.403
Q31.1239
95-th percentile1.7017
Maximum1.8821
Range2.9768
Interquartile range (IQR)1.4579

Descriptive statistics

Standard deviation0.8330219485
Coefficient of variation (CV)2.085746501
Kurtosis-1.19970164
Mean0.399387916
Median Absolute Deviation (MAD)0.7282
Skewness0.01803080251
Sum2891.9679
Variance0.6939255667
MonotocityNot monotonic
2020-12-19T02:26:41.292309image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.715740.1%
 
0.368440.1%
 
-0.68973< 0.1%
 
-0.90243< 0.1%
 
0.26433< 0.1%
 
0.33343< 0.1%
 
1.56123< 0.1%
 
1.58333< 0.1%
 
-0.61463< 0.1%
 
1.70173< 0.1%
 
Other values (6398)720999.6%
 
ValueCountFrequency (%) 
-1.09471< 0.1%
 
-1.08971< 0.1%
 
-1.07681< 0.1%
 
-1.0681< 0.1%
 
-1.0671< 0.1%
 
ValueCountFrequency (%) 
1.88211< 0.1%
 
1.8771< 0.1%
 
1.87641< 0.1%
 
1.87211< 0.1%
 
1.87021< 0.1%
 

Latitude
Real number (ℝ)

ZEROS

Distinct51
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.320950145
Minimum-25
Maximum25
Zeros95
Zeros (%)1.3%
Memory size56.6 KiB
2020-12-19T02:26:41.481556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-25
5-th percentile-23
Q1-17
median-1
Q316
95-th percentile23
Maximum25
Range50
Interquartile range (IQR)33

Descriptive statistics

Standard deviation16.5531294
Coefficient of variation (CV)-51.57539155
Kurtosis-1.476958296
Mean-0.320950145
Median Absolute Deviation (MAD)16
Skewness0.02924320676
Sum-2324
Variance274.0060928
MonotocityNot monotonic
2020-12-19T02:26:41.658782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-223344.6%
 
223244.5%
 
-233194.4%
 
232994.1%
 
-212363.3%
 
212233.1%
 
-242233.1%
 
242132.9%
 
-201972.7%
 
201822.5%
 
Other values (41)469164.8%
 
ValueCountFrequency (%) 
-25600.8%
 
-242233.1%
 
-233194.4%
 
-223344.6%
 
-212363.3%
 
ValueCountFrequency (%) 
25590.8%
 
242132.9%
 
232994.1%
 
223244.5%
 
212233.1%
 

Longitude
Real number (ℝ)

Distinct361
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.07982322884
Minimum-180
Maximum180
Zeros32
Zeros (%)0.4%
Memory size56.6 KiB
2020-12-19T02:26:41.856496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-180
5-th percentile-162
Q1-91
median0
Q390
95-th percentile162
Maximum180
Range360
Interquartile range (IQR)181

Descriptive statistics

Standard deviation103.9374904
Coefficient of variation (CV)-1302.095793
Kurtosis-1.20082701
Mean-0.07982322884
Median Absolute Deviation (MAD)90
Skewness-0.002260916181
Sum-578
Variance10803.00191
MonotocityNot monotonic
2020-12-19T02:26:42.039341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-107340.5%
 
161330.5%
 
0320.4%
 
-32320.4%
 
-64320.4%
 
87310.4%
 
36310.4%
 
-137310.4%
 
-99310.4%
 
61300.4%
 
Other values (351)692495.6%
 
ValueCountFrequency (%) 
-18050.1%
 
-179230.3%
 
-178140.2%
 
-177200.3%
 
-176240.3%
 
ValueCountFrequency (%) 
180150.2%
 
179180.2%
 
178210.3%
 
177220.3%
 
176260.4%
 

Penumbral Eclipse Duration (m)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2548
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.9004281
Minimum7.4
Maximum379.1
Zeros0
Zeros (%)0.0%
Memory size56.6 KiB
2020-12-19T02:26:42.228333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7.4
5-th percentile101.9
Q1222.7
median294.9
Q3327.5
95-th percentile365
Maximum379.1
Range371.7
Interquartile range (IQR)104.8

Descriptive statistics

Standard deviation79.99001822
Coefficient of variation (CV)0.296368623
Kurtosis0.1877322498
Mean269.9004281
Median Absolute Deviation (MAD)44.6
Skewness-0.9711499305
Sum1954349
Variance6398.403015
MonotocityNot monotonic
2020-12-19T02:26:42.408523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
318.4150.2%
 
336130.2%
 
319.6130.2%
 
320.3130.2%
 
317.3120.2%
 
319.7120.2%
 
320.9120.2%
 
317120.2%
 
318120.2%
 
302.2110.2%
 
Other values (2538)711698.3%
 
ValueCountFrequency (%) 
7.41< 0.1%
 
8.11< 0.1%
 
11.81< 0.1%
 
12.91< 0.1%
 
141< 0.1%
 
ValueCountFrequency (%) 
379.12< 0.1%
 
3791< 0.1%
 
378.91< 0.1%
 
378.81< 0.1%
 
378.71< 0.1%
 

Interactions

2020-12-19T02:25:51.668128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:25:51.831411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:25:51.988241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:25:52.132108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:25:52.267664image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:25:52.423884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:25:52.571555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:25:52.707346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:25:52.903973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:25:53.075731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:25:53.227825image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-19T02:25:53.384834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-19T02:25:53.684750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-19T02:25:59.824894image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-19T02:26:43.048029image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-19T02:26:43.308251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-19T02:26:43.558091image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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2020-12-19T02:26:36.041448image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

YearMonthDayHoursMinutesSecondsDelta T (s)Lunation NumberSaros NumberEclipse TypeGammaPenumbral MagnitudeUmbral MagnitudeLatitudeLongitudePenumbral Eclipse Duration (m)
016241284710519-247197850.96531.11700.0567-23-139292.5
11111919313010515-247144511.37680.3449-0.681521108163.0
2112198215610514-24713831-1.40070.2866-0.711222-81144.4
325157244210510-24708505-0.97321.06470.0795-19-69262.0
421191381310505-247025550.74051.54030.45871716325.2
53542222510501-24696607-0.19392.47521.5284-1567316.7
6310291324010496-246906580.06452.79191.68831317378.7
7442315311710491-246847050.53991.84140.8926-11170299.4
841017157010486-24678755-0.62191.75350.6806812335.9
953141964910482-24673421-1.37630.3466-0.68142119167.1

Last rows

YearMonthDayHoursMinutesSecondsDelta T (s)Lunation NumberSaros NumberEclipse TypeGammaPenumbral MagnitudeUmbral MagnitudeLatitudeLongitudePenumbral Eclipse Duration (m)
72312997114195354399123311791-1.26090.5907-0.50132093225.4
7232299761254064402123361465-0.99091.04050.0389-24-66264.0
723329971251015844061234215150.93481.14510.140323-138268.5
7234299861122994410123481567-0.25362.42101.3646-22-169361.5
7235299811250381244131235416180.22102.43541.4693215316.7
7236299952113211344171236016650.49711.98530.9069-20178356.7
7237299911141641254421123661716-0.46691.98681.015318124308.0
723830005101501644241237217611.21170.6604-0.3907-17153230.6
7239300010517103844271237714331.54970.0329-1.0039611853.5
72403000114545184428123781811-1.21700.6326-0.382814-72214.8